Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Tasks

TL;DR

This paper introduces a novel LLM-based framework for real-time physical-layer optimization in 6G IoT networks, leveraging prompt engineering and feedback loops to achieve near-optimal results without retraining.

Contribution

It proposes PE-RTFV, a prompt-engineering framework that uses LLMs and real-time feedback for efficient physical-layer optimization in resource-constrained IoT systems.

Findings

Achieves near-genetic-algorithm performance within few iterations.

Demonstrates effectiveness on wireless-powered IoT testbed.

Validates real-time optimization without model retraining.

Abstract

This paper investigates the role of large language models (LLMs) in sixth-generation (6G) Internet of Things (IoT) networks and proposes a prompt-engineering-based real-time feedback and verification (PE-RTFV) framework that perform physical-layer's optimization tasks through an iteratively process. By leveraging the naturally available closed-loop feedback inherent in wireless communication systems, PE-RTFV enables real-time physical-layer optimization without requiring model retraining. The proposed framework employs an optimization LLM (O-LLM) to generate task-specific structured prompts, which are provided to an agent LLM (A-LLM) to produce task-specific solutions. Utilizing real-time system feedback, the O-LLM iteratively refines the prompts to guide the A-LLM toward improved solutions in a gradient-descent-like optimization process. We test PE-RTFV approach on wireless-powered IoT testbed case study on user-goal-driven constellation design through semantically solving rate-energy (RE)-region optimization problem which demonstrates that PE-RTFV achieves near-genetic-algorithm performance within only a few iterations, validating its effectiveness for complex physical-layer optimization tasks in resource-constrained IoT networks.